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Building Defect Prediction Models in Practice

Building Defect Prediction Models in Practice
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Author(s): Rudolf Ramler (Software Competence Center Hagenberg, Austria), Johannes Himmelbauer (Software Competence Center Hagenberg, Austria) and Thomas Natschläger (Software Competence Center Hagenberg, Austria)
Copyright: 2014
Pages: 26
Source title: Handbook of Research on Emerging Advancements and Technologies in Software Engineering
Source Author(s)/Editor(s): Imran Ghani (Universiti Teknologi Malaysia, Malaysia), Wan Mohd Nasir Wan Kadir (Universiti Teknologi Malaysia, Malaysia) and Mohammad Nazir Ahmad (Universiti Teknologi Malaysia, Malaysia)
DOI: 10.4018/978-1-4666-6026-7.ch024

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Abstract

The information about which modules of a future version of a software system will be defect-prone is a valuable planning aid for quality managers and testers. Defect prediction promises to indicate these defect-prone modules. In this chapter, building a defect prediction model from data is characterized as an instance of a data-mining task, and key questions and consequences arising when establishing defect prediction in a large software development project are discussed. Special emphasis is put on discussions on how to choose a learning algorithm, select features from different data sources, deal with noise and data quality issues, as well as model evaluation for evolving systems. These discussions are accompanied by insights and experiences gained by projects on data mining and defect prediction in the context of large software systems conducted by the authors over the last couple of years. One of these projects has been selected to serve as an illustrative use case throughout the chapter.

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